SC385-6-FY-CO:
Models and Measurement in Quantitative Sociology
2020/21
Sociology and Criminology
Colchester Campus
Full Year
Undergraduate: Level 6
Current
Thursday 08 October 2020
Friday 02 July 2021
30
28 April 2021
Requisites for this module
SC203 or GV207 or SC208
(none)
(none)
(none)
(none)
BSC L315 Sociology (Applied Quantitative Research),
BSC L316 Sociology (Applied Quantitative Research) (Including Year Abroad),
BSC L317 Sociology (Applied Quantitative Research) (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (Including foundation Year)
The first term of the module is focused on statistical models and begins with simple OLS regression and provides a framework for modelling strategy and variable selection. Students are then taken through extensions to the basic OLS model, with categorical predictors, interactions and non-linear terms. Next, we introduce models for categorical outcomes: binary logistic and multinomial logit. The term concludes with a discussion of practical topics in survey data analysis – how to deal with complex sample designs, weighting and non-response adjustments. The modelling framework outlined in this term builds the foundations for advanced quantitative social science methods.
The second term of the module introduces students to the data science concepts, techniques, and skills necessary to perform reproducible data analysis of variety of quantitative social data. Students will engage in hands-on reproducible data analysis workflow using open source computational tools, including the Python programming language, JupyterLab (and Jupyter Notebook), Markdown, GitHub, and the Open Science Framework (OSF). Prior knowledge of programming is not required and students that experience difficulties in installing software will have the opportunity to access it online from their laptops, tablets, or smartphones via JupyterHub. The students will learn, in an accessible way, basic models for machine learning, causal inference, and network analysis as well as practical data science skills, including data wrangling and visualization of various big data sources. The content is organized around three fundamental data science tasks--description (and exploratory data analysis), prediction, and causal inference (which includes experimental design). Attention is given to model evaluation and problems of selection bias, measurement error, confounding, and overfitting. Throughout the course are discussed issues of ethics, privacy, and fairness of quantitative models in social sciences.
This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software.
Transferable skills and learning outcomes
By the end of the module, you will be able to:
Perform, critically interpret, and communicate results from analysis using OLS regression, including models with categorical predictors, interactions, and non-linear terms.
• Perform, critically interpret, and communicate results from analysis using logistic and multinomial logit models.
• Deal with practical issues of data analysis, including complex sample designs, weighting and non-response adjustments.
• Freely and flexibly use computational tools—Python, Jupyter, Markdown—to perform reproducible data analysis and communicate your results.
• Wrangle, explore, visualize, and model your dataset using various Python libraries.
• Build an open and reproducible research workflow ranging from raw data to research report.
• Perform, critically interpret, and communicate results from analysis using basic models for machine learning, causal inference, and network analysis.
• Identify and deal with issues of selection bias, measurement error, confounding, and overfitting.
• Articulate and address issues of ethics, privacy, and fairness of quantitative models in the social domain.
• Write a clean, reusable code in Python.
• Use GitHub and OSF to share your work and collaborate on research projects with others.
If you wish to take this module but have not taken the second year module 'Researching Social Life II' (SC203-5-FY), please contact the module supervisor to see if you have the appropriate background in statistics.
No information available.
- Field, Andy P. (2018) Discovering statistics using IBM SPSS statistics, London: SAGE.
- Field, Andy P. (2018) 'Correlation', in Discovering statistics using IBM SPSS statistics, London: SAGE.
The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Online Quiz |
|
15% |
| Coursework |
Data Analysis Report |
|
35% |
| Coursework |
Data Analysis Exercise 1 |
|
10% |
| Coursework |
Data Analysis Exercise 2 |
|
10% |
| Coursework |
Data Analysis Report |
|
30% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Prof Nick Allum, email: nallum@essex.ac.uk.
Dr Valentin Danchev, email: valentin.danchev@essex.ac.uk.
Professor Nick Allum, Dr Valentin Danchev
Jane Harper, Undergraduate Administrator, Telephone: 01206 873052
E-mail: socugrad@essex.ac.uk
Yes
Yes
No
Dr Jennifer Fleetwood
Goldsmiths, University of London
Senior Lecturer in Criminology
Available via Moodle
Of 3133 hours, 8 (0.3%) hours available to students:
3125 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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